First, we need behavioral anomaly detection for AI systems. Traditional observability focuses on latency, errors, and resource utilization. But AI systems require a different lens to detect when behavior deviates from expectations, even when no explicit “error” occurs.
Second, we need tamper-proof audit trails. As AI systems take on more responsibility, you have to be able to reconstruct decisions. Teams need to understand what happened and, more importantly, why. And they need to trust that the data hasn’t been altered.
Third, observability must become dynamic and adaptive. Static dashboards and predefined metrics won’t cut it. AI systems operate in constantly changing environments, and observability must be able to:
